说明：  首先介绍了图像特征向量维度过高的问题以及图像特征降维处理。在讨论Zernike矩基 本概念以及图像Zernike矩形状特征向量表示的基础上,指出Zernike矩特征向量一般都是高维的。 在介绍主成分分析方法的基础上,指出可以将其应用到Zernike矩特征向量的降维中,并给出了降维 的处理过程。最后的实验结果证明了该方法的可行性。
(Higher dimension of image feature is the critical p roblem and the dimension reduction is the most important phase in image p rocessing. Itwas pointed out that the dimension of Zernike moments feature vector was generally high after briefly introducing the basic concep t of the Zernike moments and the image Zernike moments shape feature vector. Based on the p rincipal components analysis, itwas shown that the p rincipal components analysis (PCA) could be app lied in dimension reduction of image Zernike moments feature. Meanwhile, the p rocess of the dimension reduction based on PCA was put forward. The experimental results demonstrate the feasibility of the app lication.)